Machine Learning in Python Bootcamp with 5 Capstone Projects
Master Machine Learning Algorithms and Models in Python with hands-on Projects in Data Science. Code workbooks included.
What you’ll learn
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Theory and practical implementation of linear regression using sklearn
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Theory and practical implementation of logistic regression using sklearn
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Feature selection using RFECV
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Data transformation with linear and logistic regression.
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Evaluation metrics to analyze the performance of models
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Industry relevance of linear and logistic regression
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Mathematics behind KNN, SVM and Naive Bayes algorithms
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Implementation of KNN, SVM and Naive Bayes using sklearn
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Attribute selection methods- Gini Index and Entropy
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Mathematics behind Decision trees and random forest
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Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost
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Different Algorithms for Clustering
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Different methods to deal with imbalanced data
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Correlation Filtering
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Variance Filtering
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PCA & LDA
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Content and Collaborative based filtering
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Singular Value Decomposition
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Different algorithms used for Time Series forecasting
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Case studies
Requirements
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To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language.
Who this course is for:
- Anyone who has already started their data science journey and now wanting to master machine learning.
- This course is for machine learning beginners as well as intermediates.
